Fredericton is the Capital City of the only Canadian fully-bilingual Province of New Brunswick and is beautifully located on the banks of the Saint John River. While one of the least populated provincial capital cities with a population base of less than 60 thousand residents, it offers a wide spectrum of venues and is a governement, university and cultural hub.
As the city grows and develops, it becomes increasingly important to examine and understand it quantitiatively. The City of Fredericton provides open data for everyone and encourages entrepreneurial use to develop services for the benefit of its ciitzens.
Developers, investors, policy makers and/or city planners have an interest in answering the following questions as the need for additional services and citizen protection:
Let's find out.
from PIL import Image
import requests
url = 'http://www.tourismfredericton.ca/sites/default/files/field/image/fredericton.jpg'
im = Image.open(requests.get(url, stream=True).raw)
im
Using this data will allow exploration and examination to answer the questions. The neighbourhood data will enable us to properly group crime by neighbourhood. The Census data will enable us to then compare the population density to examine if areas of highest crime are also most densely populated. Fredericton locations of interest will then allow us to cluster and quantitatively understand the venues most common to that location.
This is going to be interesting...
All steps are referenced beleow in the Appendix: Analysis section.
The methodology will include:
After loading the applicable libraries, the referenced geojson neighbourhood data was loaded from the City of Fredericton Open Data site. This dataset uses block polygon shape coordinates which are better for visualization and comparison. The City also uses Ward data but the Neighbourhood location data is more accurate and includes more details. The same type of dataset was then loaded for the population density from the Stats Canada Census tracts.
The third dataset, an excel file, "Crime by Neighbourhood 2017" downloaded from the City of Fredericton Open Data site is found under the Public Safety domain. This dataset was then uploaded for the analysis. It's interesting to note the details of this dataset are aggregated by neighbourhood. It is not an exhaustive set by not including all crimes (violent offenses) nor specific location data of the crime but is referenced by neighbourhood.
This means we can gain an understanding of the crime volume by type by area but not specific enough to understand the distribution properties. Valuable questions such as, "are these crimes occuring more often in a specific area and at a certain time by a specific demographic of people?" cannot be answered nor explored due to what is reasonably assumed to be personal and private information with associated legal risks.
There is value to the city to explore the detailed crime data using data science to predict frequency, location, timing and conditions to best allocated resources for the benefit of its citizens and it's police force. However, human behaviour is complex requiring thick profile data by individual and the conditions surrounding the event(s). To be sufficient for reliable future prediction it would need to demonstrate validity, currency, reliability and sufficiency.
Exploring the count of crimes by neighbourhood gives us the first glimpse into the distribution.
One note is the possibility neighbourhoods names could change at different times. The crime dataset did not mention which specific neighbourhood naming dataset it was using but we assumed the neighbourhood data provided aligned with the neighbourhoods used in the crime data. It may be beneficial for the City to note and timestamp neighbourhood naming in the future or simply reference with neighbourhood naming file it used for the crime dataset.
An example of data errors: There was an error found in the naming of the neighbourhood "Platt". The neighbourhood data stated "Plat" while the crime data stated "Platt". Given the crime dataset was most simple to manipulate it was modified to "Plat". The true name of the neighbourhood is "Platt".
Once the data was prepared, a choropleth map was created to view the crime count by neighbourhood. As expected the region of greatest crime count was found in the downtown and Platt neighbourhoods.
Examining the crime types enables us to learn the most frequent occuring crimes which we then plot as a bar chart to see most frequenty type.
Theft from motor vehicles is most prevalent in the same area as the most frequent crimes. It's interesting to note this area is mostly residential and most do not have garages. It would be interesting to further examine if surveillance is a deterant for motor vehicle crimes in the downtown core compared to low surveillance in the Platt neighbourhood.
After exploring the pivot table showing Crime_Type by Neighbourhood, we drill into a specific type of crime, theft from vehicles and plot the choropleth map to see which area has the greatest frequency.
Again, the Platt neighbourhood appears as the most frequent.
Is this due to population density?
Visualising the population density enables us to determine that the Platt neighbourhood has lower correlation to crime frequency than I would have expected.
It would be interesting to further study the Census data and if this captures the population that is renting or more temporary/transient poplution, given the City is a University hub.
Loading the "Fredericton Locations" data enables us to perform a statistical analysis on the most common venues by location.
We might wonder if the prevalence of bars and clubs in the downtown region has something to do with the higher crime rate in the near Platt region.
Plotting the latitude and longitude coordinates of the locations of interest onto the crime choropleth map enables us to now study the most common venues by using the Foursquare data.
Grouping rows by location and the mean of the frequency of occurance of each category we venue categories we study the top five most common venues.
Putting this data into a pandas dataframe we can then determine the most common venues by location and plot onto a map.
The analysis enabled us to discover and describe visually and quantitatively:
Neighbourhoods in Fredericton
Crime freqency by neighbourhood
Crime type frequency and statistics. The mean crime count in the City of Fredericton is 22.
Crime type count by neighbourhood. Theft from motor vehicles is most prevalent in the same area as the most frequent crimes. It's interesting to note this area is mostly residential and most do not have garages. It would be interesting to further examine if surveillance is a deterant for motor vehicle crimes in the downtown core compared to low surveillance in the Platt neighbourhood.
4.1 Motor Vehicle crimes less than $5000 analysis by neighbourhood and resulting statistics. The most common crime is Other Theft less than 5k followed by Motor Vehicle Theft less than 5k. There is a mean of 6 motor vehicle thefts less than 5k by neighbourhood in the City.
4.2 That population density and resulting visual correlation is not strongly correlated to crime frequency. Causation for crime is not able to be determined given lack of open data specificity by individual and environment.
4.3 Using k-menas, we were able to determine the top 10 most common venues within a 1 km radius of the centroid of the highest crime neighbourhood. The most common venues in the highest crime neighbourhood are coffee shops followed by Pubs and Bars.
While, it is not valid, consistent, reliable or sufficient to assume a higher concentration of the combination of coffee shops, bars and clubs predicts the amount of crime occurance in the City of Fredericton, this may be a part of the model needed to be able to in the future.
We were able to determine the top 10 most common venues by location of interest.
Statisically, we determined there are no coffee shops within the Knowledge Park clusters.
The City of Fredericton Open Data enables us to gain an understanding of the crime volume by type by area but not specific enough to understand the distribution properties. Valuable questions such as, "are these crimes occuring more often in a specific area and at a certain time by a specific demographic of people?" cannot be answered nor explored due to what is reasonably assumed to be personal and private information with associated legal risks.
There is value to the city to explore the detailed crime data using data science to predict frequency, location, timing and conditions to best allocated resources for the benefit of its citizens and it's police force. However, human behaviour is complex requiring thick profile data by individual and the conditions surrounding the event(s). To be sufficient for reliable future prediction it would need to demonstrate validity, currency, reliability and sufficiency.
A note of caution is the possibility neighbourhoods names could change. The crime dataset did not mention which specific neighbourhood naming dataset it was using but we assumed the neighbourhood data provided aligned with the neighbourhoods used in the crime data. It may be beneficial for the City to note and timestamp neighbourhood naming in the future or simply reference with neighbourhood naming file it used for the crime dataset.
Errors exist in the current open data. An error was found in the naming of the neighbourhood "Platt". The neighbourhood data stated "Plat" while the crime data stated "Platt". Given the crime dataset was most simple to manipulate it was modified to "Plat". The true name of the neighbourhood is "Platt".
Theft from motor vehicles is most prevalent in the same area as the most frequent crimes. It is interesting to note this area is mostly residential and most do not have garages. It would be interesting to further examine if surveillance is a deterant for motor vehicle crimes in the downtown core compared to low surveillance in the Platt neighbourhood.
It would be interesting to further study the Census data and if this captures the population that is renting or more temporary/transient poplution, given the City is a University hub.
Given the findings of the top 10 most frequent venues by locations of interest, the Knowledge Park does not have Coffee Shops in the top 10 most common venues as determined from the Foursquare dataset. Given this area has the greatest concentration of stores and shops as venues, it would be safe to assume a coffee shop would be beneficial to the business community and the citizens of Fredericton.
Using a combination of datasets from the City of Fredericton Open Data project and Foursquare venue data we were able to analyse, discover and describe neighbhourhoods, crime, population density and statistically describe quantitatively venues by locations of interest.
While overall, the City of Fredericton Open Data is interesting, it misses the details required for true valued quantitiatve analysis and predictive analytics which would be most valued by investors and developers to make appropriate investments and to minimize risk.
The Open Data project is a great start and empowers the need for a "Citizens Like Me" model to be developed where citizens of digital Fredericton are able to share their data as they wish for detailed analysis that enables the creation of valued services.
import numpy as np # library to handle data in a vectorized manner
import pandas as pd # library for data analysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
import json # library to handle JSON files
!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values
import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe
# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
# import k-means from clustering stage
from sklearn.cluster import KMeans
# for webscraping import Beautiful Soup
from bs4 import BeautifulSoup
import xml
!conda install -c conda-forge folium=0.5.0 --yes
import folium # map rendering library
print('Libraries imported.')
Solving environment: done
## Package Plan ##
environment location: /home/jupyterlab/conda
added / updated specs:
- geopy
The following packages will be downloaded:
package | build
---------------------------|-----------------
geographiclib-1.49 | py_0 32 KB conda-forge
cryptography-2.4.2 | py36h1ba5d50_0 618 KB
openssl-1.1.1a | h14c3975_1000 4.0 MB conda-forge
libarchive-3.3.3 | h5d8350f_5 1.5 MB
grpcio-1.16.1 | py36hf8bcb03_1 1.1 MB
geopy-1.18.1 | py_0 51 KB conda-forge
conda-4.6.2 | py36_0 869 KB conda-forge
libssh2-1.8.0 | 1 239 KB conda-forge
python-3.6.8 | h0371630_0 34.4 MB
------------------------------------------------------------
Total: 42.7 MB
The following NEW packages will be INSTALLED:
geographiclib: 1.49-py_0 conda-forge
The following packages will be UPDATED:
conda: 4.5.12-py36_1000 conda-forge --> 4.6.2-py36_0 conda-forge
cryptography: 2.3.1-py36hb7f436b_1000 conda-forge --> 2.4.2-py36h1ba5d50_0
curl: 7.63.0-h646f8bb_1000 conda-forge --> 7.63.0-hbc83047_1000
geopy: 1.11.0-py36_0 conda-forge --> 1.18.1-py_0 conda-forge
grpcio: 1.16.0-py36h4f00d22_1000 conda-forge --> 1.16.1-py36hf8bcb03_1
libcurl: 7.63.0-h01ee5af_1000 conda-forge --> 7.63.0-h20c2e04_1000
openssl: 1.0.2p-h14c3975_1002 conda-forge --> 1.1.1a-h14c3975_1000 conda-forge
pycurl: 7.43.0.2-py36hb7f436b_0 --> 7.43.0.2-py36h1ba5d50_0
python: 3.6.6-hd21baee_1003 conda-forge --> 3.6.8-h0371630_0
qt: 5.9.6-h8703b6f_2 --> 5.9.7-h5867ecd_1
The following packages will be DOWNGRADED:
krb5: 1.16.2-hc83ff2d_1000 conda-forge --> 1.16.1-h173b8e3_7
libarchive: 3.3.3-ha149a29_1000 conda-forge --> 3.3.3-h5d8350f_5
libssh2: 1.8.0-h1ad7b7a_1003 conda-forge --> 1.8.0-1 conda-forge
Downloading and Extracting Packages
geographiclib-1.49 | 32 KB | ##################################### | 100%
cryptography-2.4.2 | 618 KB | ##################################### | 100%
openssl-1.1.1a | 4.0 MB | ##################################### | 100%
libarchive-3.3.3 | 1.5 MB | ##################################### | 100%
grpcio-1.16.1 | 1.1 MB | ##################################### | 100%
geopy-1.18.1 | 51 KB | ##################################### | 100%
conda-4.6.2 | 869 KB | ##################################### | 100%
libssh2-1.8.0 | 239 KB | ##################################### | 100%
python-3.6.8 | 34.4 MB | ##################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Collecting package metadata: done
Solving environment: done
# All requested packages already installed.
Libraries imported.
r = requests.get('https://opendata.arcgis.com/datasets/823d86e17a6d47808c6e4f1c2dd97928_0.geojson')
fredericton_geo = r.json()
neighborhoods_data = fredericton_geo['features']
neighborhoods_data[0]
{'type': 'Feature',
'properties': {'FID': 1,
'OBJECTID': 1,
'Neighbourh': 'Fredericton South',
'Shape_Leng': 40412.2767429,
'Shape_Area': 32431889.0002},
'geometry': {'type': 'Polygon',
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g = requests.get('https://opendata.arcgis.com/datasets/6179d35eacb144a5b5fdcc869f86dfb5_0.geojson')
demog_geo = g.json()
demog_data = demog_geo['features']
demog_data[0]
{'type': 'Feature',
'properties': {'FID': 1,
'OBJECTID': 501,
'DBUID': '1310024304',
'DAUID': '13100243',
'CDUID': '1310',
'CTUID': '3200002.00',
'CTNAME': '0002.00',
'DBuid_1': '1310024304',
'DBpop2011': 60,
'DBtdwell20': 25,
'DBurdwell2': 22,
'Shape_Leng': 0.00746165241824,
'Shape_Area': 2.81310751889e-06,
'CTIDLINK': 3200002,
'Shape__Area': 2.81310897700361e-06,
'Shape__Length': 0.00746165464503067},
'geometry': {'type': 'Polygon',
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crime_df = pd.read_csv("Crime_by_neighbourhood_2017.csv")
crime_df.head()
| Neighbourhood | From_Date | To_Date | Crime_Code | Crime_Type | Ward | City | FID | |
|---|---|---|---|---|---|---|---|---|
| 0 | Fredericton South | 2017-01-05T00:00:00.000Z | 2017-01-26T00:00:00.000Z | 2120 | B&E NON-RESIDNCE | 7 | Fredericton | 1 |
| 1 | Fredericton South | 2017-03-04T00:00:00.000Z | 2017-03-06T00:00:00.000Z | 2120 | B&E NON-RESIDNCE | 7 | Fredericton | 2 |
| 2 | Fredericton South | 2017-05-07T00:00:00.000Z | NaN | 2120 | B&E NON-RESIDNCE | 12 | Fredericton | 3 |
| 3 | Fredericton South | 2017-06-20T00:00:00.000Z | 2017-06-21T00:00:00.000Z | 2120 | B&E NON-RESIDNCE | 12 | Fredericton | 4 |
| 4 | Fredericton South | 2017-07-09T00:00:00.000Z | 2017-07-10T00:00:00.000Z | 2120 | B&E NON-RESIDNCE | 7 | Fredericton | 5 |
crime_df.drop(['From_Date', 'To_Date'], axis=1,inplace=True)
crime_data = crime_df.groupby(['Neighbourhood']).size().to_frame(name='Count').reset_index()
crime_data
| Neighbourhood | Count | |
|---|---|---|
| 0 | Barkers Point | 47 |
| 1 | Brookside | 54 |
| 2 | Brookside Estates | 9 |
| 3 | Brookside Mini Home Park | 5 |
| 4 | College Hill | 41 |
| 5 | Colonial heights | 9 |
| 6 | Cotton Mill Creek | 4 |
| 7 | Diamond Street | 1 |
| 8 | Doak Road | 1 |
| 9 | Douglas | 3 |
| 10 | Downtown | 127 |
| 11 | Dun's Crossing | 18 |
| 12 | Forest Hill | 12 |
| 13 | Fredericton South | 85 |
| 14 | Fulton Heights | 36 |
| 15 | Garden Creek | 13 |
| 16 | Garden Place | 4 |
| 17 | Gilridge Estates | 3 |
| 18 | Golf Club | 7 |
| 19 | Grasse Circle | 1 |
| 20 | Greenwood Minihome Park | 2 |
| 21 | Hanwell North | 8 |
| 22 | Heron Springs | 3 |
| 23 | Highpoint Ridge | 5 |
| 24 | Kelly's Court Minihome Park | 1 |
| 25 | Knob Hill | 4 |
| 26 | Knowledge Park | 1 |
| 27 | Lian / Valcore | 7 |
| 28 | Lincoln | 13 |
| 29 | Lincoln Heights | 14 |
| 30 | Main Street | 78 |
| 31 | Marysville | 39 |
| 32 | McKnight | 4 |
| 33 | McLeod Hill | 3 |
| 34 | Monteith / Talisman | 12 |
| 35 | Montogomery / Prospect East | 16 |
| 36 | Nashwaaksis | 25 |
| 37 | Nethervue Minihome Park | 1 |
| 38 | North Devon | 113 |
| 39 | Northbrook Heights | 10 |
| 40 | Platt | 198 |
| 41 | Poet's Hill | 4 |
| 42 | Prospect | 81 |
| 43 | Rail Side | 3 |
| 44 | Regiment Creek | 1 |
| 45 | Royal Road | 7 |
| 46 | Saint Mary's First Nation | 25 |
| 47 | Saint Thomas University | 1 |
| 48 | Sandyville | 9 |
| 49 | Serenity Lane | 2 |
| 50 | Shadowood Estates | 5 |
| 51 | Silverwood | 12 |
| 52 | Skyline Acrea | 27 |
| 53 | South Devon | 68 |
| 54 | Southwood Park | 16 |
| 55 | Springhill | 1 |
| 56 | Sunshine Gardens | 10 |
| 57 | The Hill | 44 |
| 58 | The Hugh John Flemming Forestry Center | 3 |
| 59 | University Of New Brunswick | 15 |
| 60 | Waterloo Row | 9 |
| 61 | Wesbett / Case | 1 |
| 62 | West Hills | 5 |
| 63 | Williams / Hawkins Area | 17 |
| 64 | Woodstock Road | 41 |
| 65 | Youngs Crossing | 16 |
crime_data.describe()
| Count | |
|---|---|
| count | 66.000000 |
| mean | 22.121212 |
| std | 34.879359 |
| min | 1.000000 |
| 25% | 3.000000 |
| 50% | 9.000000 |
| 75% | 23.250000 |
| max | 198.000000 |
crime_data.rename(index=str, columns={'Neighbourhood':'Neighbourh','Count':'Crime_Count'}, inplace=True)
crime_data
| Neighbourh | Crime_Count | |
|---|---|---|
| 0 | Barkers Point | 47 |
| 1 | Brookside | 54 |
| 2 | Brookside Estates | 9 |
| 3 | Brookside Mini Home Park | 5 |
| 4 | College Hill | 41 |
| 5 | Colonial heights | 9 |
| 6 | Cotton Mill Creek | 4 |
| 7 | Diamond Street | 1 |
| 8 | Doak Road | 1 |
| 9 | Douglas | 3 |
| 10 | Downtown | 127 |
| 11 | Dun's Crossing | 18 |
| 12 | Forest Hill | 12 |
| 13 | Fredericton South | 85 |
| 14 | Fulton Heights | 36 |
| 15 | Garden Creek | 13 |
| 16 | Garden Place | 4 |
| 17 | Gilridge Estates | 3 |
| 18 | Golf Club | 7 |
| 19 | Grasse Circle | 1 |
| 20 | Greenwood Minihome Park | 2 |
| 21 | Hanwell North | 8 |
| 22 | Heron Springs | 3 |
| 23 | Highpoint Ridge | 5 |
| 24 | Kelly's Court Minihome Park | 1 |
| 25 | Knob Hill | 4 |
| 26 | Knowledge Park | 1 |
| 27 | Lian / Valcore | 7 |
| 28 | Lincoln | 13 |
| 29 | Lincoln Heights | 14 |
| 30 | Main Street | 78 |
| 31 | Marysville | 39 |
| 32 | McKnight | 4 |
| 33 | McLeod Hill | 3 |
| 34 | Monteith / Talisman | 12 |
| 35 | Montogomery / Prospect East | 16 |
| 36 | Nashwaaksis | 25 |
| 37 | Nethervue Minihome Park | 1 |
| 38 | North Devon | 113 |
| 39 | Northbrook Heights | 10 |
| 40 | Platt | 198 |
| 41 | Poet's Hill | 4 |
| 42 | Prospect | 81 |
| 43 | Rail Side | 3 |
| 44 | Regiment Creek | 1 |
| 45 | Royal Road | 7 |
| 46 | Saint Mary's First Nation | 25 |
| 47 | Saint Thomas University | 1 |
| 48 | Sandyville | 9 |
| 49 | Serenity Lane | 2 |
| 50 | Shadowood Estates | 5 |
| 51 | Silverwood | 12 |
| 52 | Skyline Acrea | 27 |
| 53 | South Devon | 68 |
| 54 | Southwood Park | 16 |
| 55 | Springhill | 1 |
| 56 | Sunshine Gardens | 10 |
| 57 | The Hill | 44 |
| 58 | The Hugh John Flemming Forestry Center | 3 |
| 59 | University Of New Brunswick | 15 |
| 60 | Waterloo Row | 9 |
| 61 | Wesbett / Case | 1 |
| 62 | West Hills | 5 |
| 63 | Williams / Hawkins Area | 17 |
| 64 | Woodstock Road | 41 |
| 65 | Youngs Crossing | 16 |
crime_data.rename({'Platt': 'Plat'},inplace=True)
crime_data.rename(index=str, columns={'Neighbourhood':'Neighbourh','Count':'Crime_Count'}, inplace=True)
crime_data
| Neighbourh | Crime_Count | |
|---|---|---|
| 0 | Barkers Point | 47 |
| 1 | Brookside | 54 |
| 2 | Brookside Estates | 9 |
| 3 | Brookside Mini Home Park | 5 |
| 4 | College Hill | 41 |
| 5 | Colonial heights | 9 |
| 6 | Cotton Mill Creek | 4 |
| 7 | Diamond Street | 1 |
| 8 | Doak Road | 1 |
| 9 | Douglas | 3 |
| 10 | Downtown | 127 |
| 11 | Dun's Crossing | 18 |
| 12 | Forest Hill | 12 |
| 13 | Fredericton South | 85 |
| 14 | Fulton Heights | 36 |
| 15 | Garden Creek | 13 |
| 16 | Garden Place | 4 |
| 17 | Gilridge Estates | 3 |
| 18 | Golf Club | 7 |
| 19 | Grasse Circle | 1 |
| 20 | Greenwood Minihome Park | 2 |
| 21 | Hanwell North | 8 |
| 22 | Heron Springs | 3 |
| 23 | Highpoint Ridge | 5 |
| 24 | Kelly's Court Minihome Park | 1 |
| 25 | Knob Hill | 4 |
| 26 | Knowledge Park | 1 |
| 27 | Lian / Valcore | 7 |
| 28 | Lincoln | 13 |
| 29 | Lincoln Heights | 14 |
| 30 | Main Street | 78 |
| 31 | Marysville | 39 |
| 32 | McKnight | 4 |
| 33 | McLeod Hill | 3 |
| 34 | Monteith / Talisman | 12 |
| 35 | Montogomery / Prospect East | 16 |
| 36 | Nashwaaksis | 25 |
| 37 | Nethervue Minihome Park | 1 |
| 38 | North Devon | 113 |
| 39 | Northbrook Heights | 10 |
| 40 | Platt | 198 |
| 41 | Poet's Hill | 4 |
| 42 | Prospect | 81 |
| 43 | Rail Side | 3 |
| 44 | Regiment Creek | 1 |
| 45 | Royal Road | 7 |
| 46 | Saint Mary's First Nation | 25 |
| 47 | Saint Thomas University | 1 |
| 48 | Sandyville | 9 |
| 49 | Serenity Lane | 2 |
| 50 | Shadowood Estates | 5 |
| 51 | Silverwood | 12 |
| 52 | Skyline Acrea | 27 |
| 53 | South Devon | 68 |
| 54 | Southwood Park | 16 |
| 55 | Springhill | 1 |
| 56 | Sunshine Gardens | 10 |
| 57 | The Hill | 44 |
| 58 | The Hugh John Flemming Forestry Center | 3 |
| 59 | University Of New Brunswick | 15 |
| 60 | Waterloo Row | 9 |
| 61 | Wesbett / Case | 1 |
| 62 | West Hills | 5 |
| 63 | Williams / Hawkins Area | 17 |
| 64 | Woodstock Road | 41 |
| 65 | Youngs Crossing | 16 |
address = 'Fredericton, Canada'
geolocator = Nominatim()
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Fredericton, New Brunswick is {}, {}.'.format(latitude, longitude))
/home/jupyterlab/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Using Nominatim with the default "geopy/1.18.1" `user_agent` is strongly discouraged, as it violates Nominatim's ToS https://operations.osmfoundation.org/policies/nominatim/ and may possibly cause 403 and 429 HTTP errors. Please specify a custom `user_agent` with `Nominatim(user_agent="my-application")` or by overriding the default `user_agent`: `geopy.geocoders.options.default_user_agent = "my-application"`. In geopy 2.0 this will become an exception. This is separate from the ipykernel package so we can avoid doing imports until
The geograpical coordinate of Fredericton, New Brunswick is 45.966425, -66.645813.
world_geo = r'world_countries.json' # geojson file
fredericton_1_map = folium.Map(location=[45.97, -66.65], width=1000, height=750,zoom_start=12)
fredericton_1_map
fredericton_geo = r.json()
threshold_scale = np.linspace(crime_data['Crime_Count'].min(),crime_data['Crime_Count'].max(), 6,dtype=int)
threshold_scale = threshold_scale.tolist()
threshold_scale[-1] = threshold_scale[-1]+1
fredericton_1_map.choropleth(geo_data=fredericton_geo, data=crime_data,columns=['Neighbourh', 'Crime_Count'],
key_on='feature.properties.Neighbourh', threshold_scale=threshold_scale,fill_color='YlOrRd', fill_opacity=0.7,
line_opacity=0.1, legend_name='Fredericton Neighbourhoods')
fredericton_1_map
crimetype_data = crime_df.groupby(['Crime_Type']).size().to_frame(name='Count').reset_index()
crimetype_data
| Crime_Type | Count | |
|---|---|---|
| 0 | 4 | |
| 1 | ARSON | 5 |
| 2 | ARSON BY NEG | 1 |
| 3 | ARSON-DAM.PROP. | 4 |
| 4 | B&E NON-RESIDNCE | 51 |
| 5 | B&E OTHER | 58 |
| 6 | B&E RESIDENCE | 151 |
| 7 | B&E STEAL FIREAR | 3 |
| 8 | MISCHIEF OBS USE | 1 |
| 9 | MISCHIEF TO PROP | 246 |
| 10 | MISCHIEF-DATA | 2 |
| 11 | MOTOR VEH THEFT | 40 |
| 12 | THEFT BIKE<$5000 | 63 |
| 13 | THEFT FROM MV < $5000 | 356 |
| 14 | THEFT FROM MV > $5000 | 5 |
| 15 | THEFT OTH <$5000 | 458 |
| 16 | THEFT OTH >$5000 | 9 |
| 17 | THEFT OVER $5000 | 1 |
| 18 | THEFT,BIKE>$5000 | 2 |
crimetype_data.describe()
| Count | |
|---|---|
| count | 19.000000 |
| mean | 76.842105 |
| std | 133.196706 |
| min | 1.000000 |
| 25% | 2.500000 |
| 50% | 5.000000 |
| 75% | 60.500000 |
| max | 458.000000 |
crimepivot = crime_df.pivot_table(index='Neighbourhood', columns='Crime_Type', aggfunc=pd.Series.count, fill_value=0)
crimepivot
| City | Crime_Code | FID | Ward | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Crime_Type | ARSON | ARSON BY NEG | ARSON-DAM.PROP. | B&E NON-RESIDNCE | B&E OTHER | B&E RESIDENCE | B&E STEAL FIREAR | MISCHIEF OBS USE | MISCHIEF TO PROP | MISCHIEF-DATA | MOTOR VEH THEFT | THEFT BIKE<$5000 | THEFT FROM MV < $5000 | THEFT FROM MV > $5000 | THEFT OTH <$5000 | THEFT OTH >$5000 | THEFT OVER $5000 | THEFT,BIKE>$5000 | ARSON | ARSON BY NEG | ARSON-DAM.PROP. | B&E NON-RESIDNCE | B&E OTHER | B&E RESIDENCE | B&E STEAL FIREAR | MISCHIEF OBS USE | MISCHIEF TO PROP | MISCHIEF-DATA | MOTOR VEH THEFT | THEFT BIKE<$5000 | THEFT FROM MV < $5000 | THEFT FROM MV > $5000 | THEFT OTH <$5000 | THEFT OTH >$5000 | THEFT OVER $5000 | THEFT,BIKE>$5000 | ARSON | ARSON BY NEG | ARSON-DAM.PROP. | B&E NON-RESIDNCE | B&E OTHER | B&E RESIDENCE | B&E STEAL FIREAR | MISCHIEF OBS USE | MISCHIEF TO PROP | MISCHIEF-DATA | MOTOR VEH THEFT | THEFT BIKE<$5000 | THEFT FROM MV < $5000 | THEFT FROM MV > $5000 | THEFT OTH <$5000 | THEFT OTH >$5000 | THEFT OVER $5000 | THEFT,BIKE>$5000 | ARSON | ARSON BY NEG | ARSON-DAM.PROP. | B&E NON-RESIDNCE | B&E OTHER | B&E RESIDENCE | B&E STEAL FIREAR | MISCHIEF OBS USE | MISCHIEF TO PROP | MISCHIEF-DATA | MOTOR VEH THEFT | THEFT BIKE<$5000 | THEFT FROM MV < $5000 | THEFT FROM MV > $5000 | THEFT OTH <$5000 | THEFT OTH >$5000 | THEFT OVER $5000 | THEFT,BIKE>$5000 | ||||
| Neighbourhood | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Barkers Point | 0 | 0 | 0 | 0 | 2 | 7 | 7 | 1 | 0 | 7 | 0 | 2 | 2 | 8 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 7 | 7 | 1 | 0 | 7 | 0 | 2 | 2 | 8 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 7 | 7 | 1 | 0 | 7 | 0 | 2 | 2 | 8 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 7 | 7 | 1 | 0 | 7 | 0 | 2 | 2 | 8 | 0 | 11 | 0 | 0 | 0 |
| Brookside | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 50 | 0 | 0 | 0 |
| Brookside Estates | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 0 | 2 | 0 | 0 | 0 |
| Brookside Mini Home Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| College Hill | 0 | 2 | 0 | 0 | 0 | 2 | 13 | 0 | 0 | 4 | 0 | 0 | 2 | 10 | 0 | 8 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 13 | 0 | 0 | 4 | 0 | 0 | 2 | 10 | 0 | 8 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 13 | 0 | 0 | 4 | 0 | 0 | 2 | 10 | 0 | 8 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 13 | 0 | 0 | 4 | 0 | 0 | 2 | 10 | 0 | 8 | 0 | 0 | 0 |
| Colonial heights | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 |
| Cotton Mill Creek | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Diamond Street | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Doak Road | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Douglas | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Downtown | 0 | 1 | 0 | 1 | 7 | 0 | 3 | 0 | 0 | 29 | 1 | 4 | 8 | 21 | 0 | 49 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 7 | 0 | 3 | 0 | 0 | 29 | 1 | 4 | 8 | 21 | 0 | 49 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 7 | 0 | 3 | 0 | 0 | 29 | 1 | 4 | 8 | 21 | 0 | 49 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 7 | 0 | 3 | 0 | 0 | 29 | 1 | 4 | 8 | 21 | 0 | 49 | 1 | 1 | 1 |
| Dun's Crossing | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 0 | 2 | 0 | 0 | 0 |
| Forest Hill | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 3 | 0 | 0 | 0 |
| Fredericton South | 1 | 0 | 0 | 0 | 6 | 1 | 1 | 0 | 0 | 13 | 0 | 1 | 2 | 20 | 1 | 35 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 1 | 1 | 0 | 0 | 13 | 0 | 1 | 2 | 20 | 1 | 35 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 1 | 1 | 0 | 0 | 13 | 0 | 1 | 2 | 20 | 1 | 35 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 1 | 1 | 0 | 0 | 13 | 0 | 1 | 2 | 20 | 1 | 35 | 4 | 0 | 0 |
| Fulton Heights | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 8 | 0 | 0 | 3 | 12 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 8 | 0 | 0 | 3 | 12 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 8 | 0 | 0 | 3 | 12 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | 0 | 0 | 8 | 0 | 0 | 3 | 12 | 0 | 6 | 0 | 0 | 0 |
| Garden Creek | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 | 1 | 0 | 0 |
| Garden Place | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| Gilridge Estates | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
| Golf Club | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
| Grasse Circle | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Greenwood Minihome Park | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hanwell North | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 |
| Heron Springs | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Highpoint Ridge | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 1 | 0 | 0 | 0 |
| Kelly's Court Minihome Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Knob Hill | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Knowledge Park | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Lian / Valcore | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 5 | 0 | 0 | 0 |
| Lincoln | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 | 0 |
| Lincoln Heights | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 |
| Main Street | 0 | 0 | 0 | 1 | 2 | 4 | 8 | 0 | 1 | 12 | 0 | 2 | 3 | 10 | 0 | 33 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 8 | 0 | 1 | 12 | 0 | 2 | 3 | 10 | 0 | 33 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 8 | 0 | 1 | 12 | 0 | 2 | 3 | 10 | 0 | 33 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 4 | 8 | 0 | 1 | 12 | 0 | 2 | 3 | 10 | 0 | 33 | 2 | 0 | 0 |
| Marysville | 0 | 1 | 0 | 0 | 1 | 2 | 5 | 0 | 0 | 8 | 0 | 1 | 1 | 10 | 0 | 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 5 | 0 | 0 | 8 | 0 | 1 | 1 | 10 | 0 | 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 5 | 0 | 0 | 8 | 0 | 1 | 1 | 10 | 0 | 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 5 | 0 | 0 | 8 | 0 | 1 | 1 | 10 | 0 | 10 | 0 | 0 | 0 |
| McKnight | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 |
| McLeod Hill | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Monteith / Talisman | 0 | 0 | 0 | 0 | 2 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 |
| Montogomery / Prospect East | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 11 | 0 | 0 | 0 |
| Nashwaaksis | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 3 | 0 | 0 | 5 | 0 | 1 | 0 | 9 | 1 | 3 | 0 | 0 | 0 |
| Nethervue Minihome Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| North Devon | 0 | 0 | 0 | 0 | 5 | 4 | 11 | 0 | 0 | 40 | 0 | 0 | 6 | 17 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 11 | 0 | 0 | 40 | 0 | 0 | 6 | 17 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 11 | 0 | 0 | 40 | 0 | 0 | 6 | 17 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 4 | 11 | 0 | 0 | 40 | 0 | 0 | 6 | 17 | 0 | 30 | 0 | 0 | 0 |
| Northbrook Heights | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
| Platt | 0 | 0 | 0 | 0 | 4 | 10 | 18 | 0 | 0 | 31 | 0 | 3 | 21 | 40 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 10 | 18 | 0 | 0 | 31 | 0 | 3 | 21 | 40 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 10 | 18 | 0 | 0 | 31 | 0 | 3 | 21 | 40 | 0 | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 10 | 18 | 0 | 0 | 31 | 0 | 3 | 21 | 40 | 0 | 71 | 0 | 0 | 0 |
| Poet's Hill | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Prospect | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 16 | 0 | 0 | 0 | 11 | 2 | 48 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 16 | 0 | 0 | 0 | 11 | 2 | 48 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 16 | 0 | 0 | 0 | 11 | 2 | 48 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 16 | 0 | 0 | 0 | 11 | 2 | 48 | 0 | 0 | 1 |
| Rail Side | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Regiment Creek | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Royal Road | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Saint Mary's First Nation | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 3 | 1 | 1 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 3 | 1 | 1 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 3 | 1 | 1 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 0 | 3 | 1 | 1 | 0 | 13 | 0 | 0 | 0 |
| Saint Thomas University | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Sandyville | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 |
| Serenity Lane | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Shadowood Estates | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Silverwood | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 2 | 0 | 1 | 0 | 2 | 0 | 4 | 0 | 0 | 0 |
| Skyline Acrea | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 6 | 0 | 0 | 0 | 13 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 6 | 0 | 0 | 0 | 13 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 6 | 0 | 0 | 0 | 13 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 6 | 0 | 0 | 0 | 13 | 0 | 3 | 0 | 0 | 0 |
| South Devon | 0 | 0 | 1 | 0 | 0 | 6 | 16 | 0 | 0 | 8 | 0 | 0 | 5 | 22 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 16 | 0 | 0 | 8 | 0 | 0 | 5 | 22 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 16 | 0 | 0 | 8 | 0 | 0 | 5 | 22 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 6 | 16 | 0 | 0 | 8 | 0 | 0 | 5 | 22 | 0 | 10 | 0 | 0 | 0 |
| Southwood Park | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 7 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 7 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 7 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 7 | 0 | 4 | 0 | 0 | 0 |
| Springhill | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Sunshine Gardens | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 | 0 | 1 | 0 | 0 | 0 |
| The Hill | 0 | 0 | 0 | 0 | 2 | 1 | 12 | 1 | 0 | 7 | 0 | 0 | 0 | 11 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 12 | 1 | 0 | 7 | 0 | 0 | 0 | 11 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 12 | 1 | 0 | 7 | 0 | 0 | 0 | 11 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 12 | 1 | 0 | 7 | 0 | 0 | 0 | 11 | 0 | 10 | 0 | 0 | 0 |
| The Hugh John Flemming Forestry Center | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| University Of New Brunswick | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 0 | 8 | 0 | 0 | 0 |
| Waterloo Row | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 3 | 0 | 1 | 0 | 0 | 0 |
| Wesbett / Case | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| West Hills | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Williams / Hawkins Area | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 7 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 0 | 0 |
| Woodstock Road | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 2 | 0 | 4 | 2 | 20 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 2 | 0 | 4 | 2 | 20 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 2 | 0 | 4 | 2 | 20 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 5 | 0 | 0 | 2 | 0 | 4 | 2 | 20 | 1 | 5 | 0 | 0 | 0 |
| Youngs Crossing | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 1 | 0 | 6 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 1 | 0 | 6 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 1 | 0 | 6 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 0 | 0 | 4 | 0 | 1 | 0 | 6 | 0 | 2 | 0 | 0 | 0 |
crimetype_data.plot(x='Crime_Type', y='Count', kind='barh')
<matplotlib.axes._subplots.AxesSubplot at 0x7f33ee0caa20>
mvcrime_df = crime_df.loc[crime_df['Crime_Type'] == 'THEFT FROM MV < $5000']
mvcrime_df
| Neighbourhood | Crime_Code | Crime_Type | Ward | City | FID | |
|---|---|---|---|---|---|---|
| 18 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 19 |
| 19 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 20 |
| 20 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 21 |
| 21 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 22 |
| 22 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 23 |
| 23 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 24 |
| 24 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 25 |
| 25 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 26 |
| 26 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 27 |
| 27 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 28 |
| 28 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 29 |
| 29 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 30 |
| 30 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 31 |
| 51 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 52 |
| 52 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 53 |
| 53 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 54 |
| 54 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 55 |
| 55 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 56 |
| 56 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 57 |
| 57 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 58 |
| 58 | Barkers Point | 2142 | THEFT FROM MV < $5000 | 6 | Fredericton | 59 |
| 100 | Sandyville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 101 |
| 107 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 108 |
| 108 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 109 |
| 109 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 110 |
| 110 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 111 |
| 111 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 112 |
| 112 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 113 |
| 113 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 114 |
| 114 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 115 |
| 115 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 116 |
| 116 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 117 |
| 117 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 118 |
| 118 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 119 |
| 119 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 120 |
| 120 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 121 |
| 121 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 122 |
| 122 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 123 |
| 123 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 124 |
| 124 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 125 |
| 125 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 126 |
| 126 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 127 |
| 127 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 128 |
| 128 | South Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 129 |
| 151 | Sandyville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 152 |
| 156 | Knob Hill | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 157 |
| 165 | Youngs Crossing | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 166 |
| 166 | Youngs Crossing | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 167 |
| 167 | Youngs Crossing | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 168 |
| 168 | Youngs Crossing | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 169 |
| 169 | Youngs Crossing | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 170 |
| 170 | Youngs Crossing | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 171 |
| 201 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 202 |
| 252 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 253 |
| 278 | Douglas | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 279 |
| 280 | McLeod Hill | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 281 |
| 281 | McLeod Hill | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 282 |
| 301 | Marysville | 2142 | THEFT FROM MV < $5000 | 0 | Fredericton | 302 |
| 302 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 303 |
| 303 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 304 |
| 304 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 305 |
| 305 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 306 |
| 306 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 307 |
| 307 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 308 |
| 308 | Marysville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 309 |
| 330 | Saint Mary's First Nation | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 331 |
| 349 | Sandyville | 2142 | THEFT FROM MV < $5000 | 5 | Fredericton | 350 |
| 354 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 355 |
| 355 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 356 |
| 356 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 357 |
| 357 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 358 |
| 358 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 359 |
| 359 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 360 |
| 360 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 361 |
| 361 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 362 |
| 362 | Nashwaaksis | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 363 |
| 377 | Northbrook Heights | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 378 |
| 378 | Northbrook Heights | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 379 |
| 379 | Northbrook Heights | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 380 |
| 380 | Northbrook Heights | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 381 |
| 381 | Northbrook Heights | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 382 |
| 388 | Heron Springs | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 389 |
| 389 | Heron Springs | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 390 |
| 400 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 401 |
| 401 | Downtown | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 402 |
| 402 | Downtown | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 403 |
| 403 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 404 |
| 404 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 405 |
| 405 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 406 |
| 408 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 409 |
| 410 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 411 |
| 411 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 412 |
| 412 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 413 |
| 413 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 414 |
| 414 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 415 |
| 415 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 416 |
| 416 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 417 |
| 417 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 418 |
| 418 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 419 |
| 419 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 420 |
| 420 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 421 |
| 421 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 422 |
| 422 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 423 |
| 506 | Downtown | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 507 |
| 520 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 521 |
| 521 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 522 |
| 522 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 523 |
| 523 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 524 |
| 524 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 525 |
| 525 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 526 |
| 526 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 527 |
| 527 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 528 |
| 528 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 529 |
| 529 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 530 |
| 530 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 531 |
| 531 | Fulton Heights | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 532 |
| 569 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 570 |
| 570 | Main Street | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 571 |
| 571 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 572 |
| 572 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 573 |
| 573 | Main Street | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 574 |
| 574 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 575 |
| 575 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 576 |
| 576 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 577 |
| 577 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 578 |
| 578 | Main Street | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 579 |
| 604 | Golf Club | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 605 |
| 614 | Gilridge Estates | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 615 |
| 622 | Nethervue Minihome Park | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 623 |
| 625 | Monteith / Talisman | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 626 |
| 626 | Monteith / Talisman | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 627 |
| 631 | Garden Creek | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 632 |
| 640 | Highpoint Ridge | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 641 |
| 641 | Highpoint Ridge | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 642 |
| 642 | Highpoint Ridge | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 643 |
| 643 | Highpoint Ridge | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 644 |
| 650 | Golf Club | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 651 |
| 651 | Golf Club | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 652 |
| 653 | Golf Club | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 654 |
| 752 | Golf Club | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 753 |
| 764 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 765 |
| 765 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 766 |
| 766 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 767 |
| 767 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 768 |
| 768 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 769 |
| 769 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 770 |
| 770 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 771 |
| 771 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 772 |
| 772 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 773 |
| 773 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 774 |
| 774 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 775 |
| 775 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 776 |
| 776 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 0 | Fredericton | 777 |
| 777 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 778 |
| 778 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 779 |
| 779 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 780 |
| 780 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 781 |
| 781 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 782 |
| 787 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 788 |
| 788 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 789 |
| 789 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 790 |
| 790 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 791 |
| 791 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 792 |
| 792 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 793 |
| 793 | Sunshine Gardens | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 794 |
| 809 | Platt | 2142 | THEFT FROM MV < $5000 | 0 | Fredericton | 810 |
| 810 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 811 |
| 811 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 812 |
| 812 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 813 |
| 813 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 814 |
| 814 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 815 |
| 815 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 816 |
| 816 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 817 |
| 817 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 818 |
| 818 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 819 |
| 819 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 820 |
| 820 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 821 |
| 821 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 822 |
| 822 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 823 |
| 823 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 824 |
| 824 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 825 |
| 825 | Platt | 2142 | THEFT FROM MV < $5000 | 0 | Fredericton | 826 |
| 826 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 827 |
| 827 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 828 |
| 828 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 829 |
| 829 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 830 |
| 830 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 831 |
| 831 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 832 |
| 832 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 833 |
| 833 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 834 |
| 835 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 836 |
| 836 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 837 |
| 837 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 838 |
| 838 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 839 |
| 839 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 840 |
| 840 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 841 |
| 841 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 842 |
| 842 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 843 |
| 843 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 844 |
| 844 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 845 |
| 845 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 846 |
| 846 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 847 |
| 847 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 848 |
| 848 | Platt | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 849 |
| 849 | Platt | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 850 |
| 855 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 856 |
| 856 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 857 |
| 857 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 858 |
| 865 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 866 |
| 866 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 867 |
| 867 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 868 |
| 868 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 869 |
| 869 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 870 |
| 871 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 872 |
| 875 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 876 |
| 880 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 881 |
| 881 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 882 |
| 886 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 887 |
| 887 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 888 |
| 892 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 893 |
| 893 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 894 |
| 898 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 899 |
| 899 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 900 |
| 900 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 901 |
| 901 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 902 |
| 902 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 903 |
| 903 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 904 |
| 904 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 905 |
| 905 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 906 |
| 906 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 907 |
| 907 | Skyline Acrea | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 908 |
| 913 | Poet's Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 914 |
| 914 | Poet's Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 915 |
| 922 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 923 |
| 923 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 924 |
| 924 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 925 |
| 925 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 926 |
| 926 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 927 |
| 927 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 928 |
| 928 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 929 |
| 929 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 930 |
| 930 | Dun's Crossing | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 931 |
| 938 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 939 |
| 939 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 940 |
| 940 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 941 |
| 941 | Southwood Park | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 942 |
| 946 | The Hill | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 947 |
| 947 | The Hill | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 948 |
| 948 | The Hill | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 949 |
| 949 | The Hill | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 950 |
| 950 | The Hill | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 951 |
| 951 | The Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 952 |
| 952 | The Hill | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 953 |
| 954 | The Hill | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 955 |
| 955 | The Hill | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 956 |
| 956 | The Hill | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 957 |
| 957 | The Hill | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 958 |
| 969 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 970 |
| 970 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 971 |
| 971 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 972 |
| 972 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 973 |
| 973 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 974 |
| 974 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 975 |
| 975 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 976 |
| 976 | Forest Hill | 2142 | THEFT FROM MV < $5000 | 8 | Fredericton | 977 |
| 989 | Lincoln Heights | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 990 |
| 996 | Diamond Street | 2142 | THEFT FROM MV < $5000 | 1 | Fredericton | 997 |
| 1027 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1028 |
| 1028 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1029 |
| 1029 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1030 |
| 1030 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1031 |
| 1031 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1032 |
| 1032 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1033 |
| 1033 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1034 |
| 1034 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1035 |
| 1035 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1036 |
| 1036 | College Hill | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1037 |
| 1060 | Brookside Estates | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1061 |
| 1061 | Brookside Estates | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1062 |
| 1062 | Brookside Estates | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1063 |
| 1116 | Lincoln | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 1117 |
| 1124 | Colonial heights | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1125 |
| 1125 | Colonial heights | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1126 |
| 1126 | Colonial heights | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1127 |
| 1127 | Colonial heights | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1128 |
| 1128 | Colonial heights | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1129 |
| 1129 | Colonial heights | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1130 |
| 1131 | Garden Place | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1132 |
| 1132 | Garden Place | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1133 |
| 1133 | Garden Place | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1134 |
| 1144 | Waterloo Row | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1145 |
| 1145 | Waterloo Row | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1146 |
| 1146 | Waterloo Row | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1147 |
| 1151 | University Of New Brunswick | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1152 |
| 1152 | University Of New Brunswick | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1153 |
| 1153 | University Of New Brunswick | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1154 |
| 1154 | University Of New Brunswick | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1155 |
| 1163 | Saint Thomas University | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1164 |
| 1173 | Williams / Hawkins Area | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1174 |
| 1174 | Williams / Hawkins Area | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1175 |
| 1175 | Williams / Hawkins Area | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1176 |
| 1176 | Williams / Hawkins Area | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1177 |
| 1177 | Williams / Hawkins Area | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1178 |
| 1178 | Williams / Hawkins Area | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1179 |
| 1181 | McKnight | 2142 | THEFT FROM MV < $5000 | 2 | Fredricton | 1182 |
| 1187 | Shadowood Estates | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1188 |
| 1188 | Shadowood Estates | 2142 | THEFT FROM MV < $5000 | 2 | Fredericton | 1189 |
| 1240 | Lian / Valcore | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1241 |
| 1284 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1285 |
| 1285 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1286 |
| 1286 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1287 |
| 1287 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1288 |
| 1288 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1289 |
| 1289 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1290 |
| 1290 | North Devon | 2142 | THEFT FROM MV < $5000 | 4 | Fredericton | 1291 |
| 1302 | Rail Side | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1303 |
| 1306 | Rail Side | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1307 |
| 1316 | Silverwood | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1317 |
| 1317 | Silverwood | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1318 |
| 1339 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1340 |
| 1340 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1341 |
| 1341 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1342 |
| 1342 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1343 |
| 1343 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1344 |
| 1344 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1345 |
| 1345 | Prospect | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1346 |
| 1346 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1347 |
| 1347 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1348 |
| 1348 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1349 |
| 1349 | Prospect | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1350 |
| 1369 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1370 |
| 1370 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1371 |
| 1371 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1372 |
| 1372 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1373 |
| 1377 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1378 |
| 1380 | Hanwell North | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1381 |
| 1381 | Hanwell North | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1382 |
| 1382 | Hanwell North | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1383 |
| 1387 | Montogomery / Prospect East | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1388 |
| 1388 | Montogomery / Prospect East | 2142 | THEFT FROM MV < $5000 | 11 | Fredericton | 1389 |
| 1389 | Montogomery / Prospect East | 2142 | THEFT FROM MV < $5000 | 9 | Fredericton | 1390 |
| 1403 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 7 | Fredericton | 1404 |
| 1408 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1409 |
| 1409 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1410 |
| 1410 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1411 |
| 1411 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1412 |
| 1412 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1413 |
| 1413 | Fredericton South | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1414 |
| 1420 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1421 |
| 1421 | Woodstock Road | 2142 | THEFT FROM MV < $5000 | 10 | Fredericton | 1422 |
| 1437 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1438 |
| 1438 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1439 |
| 1439 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1440 |
| 1440 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1441 |
| 1441 | North Devon | 2142 | THEFT FROM MV < $5000 | 3 | Fredericton | 1442 |
| 1459 | Monteith / Talisman | 2142 | THEFT FROM MV < $5000 | 12 | Fredericton | 1460 |
mvcrime_data = mvcrime_df.groupby(['Neighbourhood']).size().to_frame(name='Count').reset_index()
mvcrime_data
| Neighbourhood | Count | |
|---|---|---|
| 0 | Barkers Point | 8 |
| 1 | Brookside Estates | 3 |
| 2 | College Hill | 10 |
| 3 | Colonial heights | 6 |
| 4 | Diamond Street | 1 |
| 5 | Douglas | 1 |
| 6 | Downtown | 21 |
| 7 | Dun's Crossing | 9 |
| 8 | Forest Hill | 8 |
| 9 | Fredericton South | 20 |
| 10 | Fulton Heights | 12 |
| 11 | Garden Creek | 1 |
| 12 | Garden Place | 3 |
| 13 | Gilridge Estates | 1 |
| 14 | Golf Club | 5 |
| 15 | Hanwell North | 3 |
| 16 | Heron Springs | 2 |
| 17 | Highpoint Ridge | 4 |
| 18 | Knob Hill | 1 |
| 19 | Lian / Valcore | 1 |
| 20 | Lincoln | 1 |
| 21 | Lincoln Heights | 11 |
| 22 | Main Street | 10 |
| 23 | Marysville | 10 |
| 24 | McKnight | 1 |
| 25 | McLeod Hill | 2 |
| 26 | Monteith / Talisman | 3 |
| 27 | Montogomery / Prospect East | 3 |
| 28 | Nashwaaksis | 9 |
| 29 | Nethervue Minihome Park | 1 |
| 30 | North Devon | 17 |
| 31 | Northbrook Heights | 5 |
| 32 | Platt | 40 |
| 33 | Poet's Hill | 2 |
| 34 | Prospect | 11 |
| 35 | Rail Side | 2 |
| 36 | Saint Mary's First Nation | 1 |
| 37 | Saint Thomas University | 1 |
| 38 | Sandyville | 3 |
| 39 | Shadowood Estates | 2 |
| 40 | Silverwood | 2 |
| 41 | Skyline Acrea | 13 |
| 42 | South Devon | 22 |
| 43 | Southwood Park | 7 |
| 44 | Sunshine Gardens | 7 |
| 45 | The Hill | 11 |
| 46 | University Of New Brunswick | 4 |
| 47 | Waterloo Row | 3 |
| 48 | Williams / Hawkins Area | 6 |
| 49 | Woodstock Road | 20 |
| 50 | Youngs Crossing | 6 |
mvcrime_data.describe()
| Count | |
|---|---|
| count | 51.000000 |
| mean | 6.980392 |
| std | 7.457855 |
| min | 1.000000 |
| 25% | 2.000000 |
| 50% | 4.000000 |
| 75% | 10.000000 |
| max | 40.000000 |
mvcrime_data.rename({'Platt': 'Plat'},inplace=True)
mvcrime_data.rename(index=str, columns={'Neighbourhood':'Neighbourh','Count':'MVCrime_Count'}, inplace=True)
mvcrime_data
| Neighbourh | MVCrime_Count | |
|---|---|---|
| 0 | Barkers Point | 8 |
| 1 | Brookside Estates | 3 |
| 2 | College Hill | 10 |
| 3 | Colonial heights | 6 |
| 4 | Diamond Street | 1 |
| 5 | Douglas | 1 |
| 6 | Downtown | 21 |
| 7 | Dun's Crossing | 9 |
| 8 | Forest Hill | 8 |
| 9 | Fredericton South | 20 |
| 10 | Fulton Heights | 12 |
| 11 | Garden Creek | 1 |
| 12 | Garden Place | 3 |
| 13 | Gilridge Estates | 1 |
| 14 | Golf Club | 5 |
| 15 | Hanwell North | 3 |
| 16 | Heron Springs | 2 |
| 17 | Highpoint Ridge | 4 |
| 18 | Knob Hill | 1 |
| 19 | Lian / Valcore | 1 |
| 20 | Lincoln | 1 |
| 21 | Lincoln Heights | 11 |
| 22 | Main Street | 10 |
| 23 | Marysville | 10 |
| 24 | McKnight | 1 |
| 25 | McLeod Hill | 2 |
| 26 | Monteith / Talisman | 3 |
| 27 | Montogomery / Prospect East | 3 |
| 28 | Nashwaaksis | 9 |
| 29 | Nethervue Minihome Park | 1 |
| 30 | North Devon | 17 |
| 31 | Northbrook Heights | 5 |
| 32 | Platt | 40 |
| 33 | Poet's Hill | 2 |
| 34 | Prospect | 11 |
| 35 | Rail Side | 2 |
| 36 | Saint Mary's First Nation | 1 |
| 37 | Saint Thomas University | 1 |
| 38 | Sandyville | 3 |
| 39 | Shadowood Estates | 2 |
| 40 | Silverwood | 2 |
| 41 | Skyline Acrea | 13 |
| 42 | South Devon | 22 |
| 43 | Southwood Park | 7 |
| 44 | Sunshine Gardens | 7 |
| 45 | The Hill | 11 |
| 46 | University Of New Brunswick | 4 |
| 47 | Waterloo Row | 3 |
| 48 | Williams / Hawkins Area | 6 |
| 49 | Woodstock Road | 20 |
| 50 | Youngs Crossing | 6 |
world_geo = r'world_countries.json' # geojson file
fredericton_c_map = folium.Map(location=[45.91, -66.65], width=1000, height=750,zoom_start=12)
fredericton_c_map
## Motor Vehicle Crime <$5000 Count
fredericton_geo = r.json()
threshold_scale = np.linspace(mvcrime_data['MVCrime_Count'].min(), mvcrime_data['MVCrime_Count'].max(),6,dtype=int)
threshold_scale = threshold_scale.tolist()
threshold_scale[-1] = threshold_scale[-1]+1
fredericton_c_map.choropleth(geo_data=fredericton_geo,data=mvcrime_data,columns=['Neighbourh', 'MVCrime_Count'],key_on='feature.properties.Neighbourh',
threshold_scale=threshold_scale, fill_color='YlOrRd',fill_opacity=0.7,line_opacity=0.1,legend_name='Fredericton Neighbourhoods')
fredericton_c_map
demog_df = pd.read_csv("Census_Tract_Demographics.csv")
demog_df.head()
| FID | OBJECTID | DBUID | DAUID | CDUID | CTUID | CTNAME | DBuid_1 | DBpop2011 | DBtdwell20 | DBurdwell2 | Shape_Leng | Shape_Area | CTIDLINK | Shape__Area | Shape__Length | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 501 | 1310024304 | 13100243 | 1310 | 3200002.0 | 2.0 | 1310024304 | 60 | 25 | 22 | 0.007462 | 0.000003 | 3200002 | 0.000003 | 0.007462 |
| 1 | 2 | 502 | 1310032004 | 13100320 | 1310 | 3200010.0 | 10.0 | 1310032004 | 15 | 3 | 3 | 0.009008 | 0.000003 | 3200010 | 0.000003 | 0.009008 |
| 2 | 3 | 503 | 1310017103 | 13100171 | 1310 | 3200014.0 | 14.0 | 1310017103 | 0 | 0 | 0 | 0.010602 | 0.000007 | 3200014 | 0.000007 | 0.010602 |
| 3 | 4 | 504 | 1310018301 | 13100183 | 1310 | 3200012.0 | 12.0 | 1310018301 | 108 | 60 | 50 | 0.039599 | 0.000068 | 3200012 | 0.000068 | 0.039599 |
| 4 | 5 | 505 | 1310022905 | 13100229 | 1310 | 3200007.0 | 7.0 | 1310022905 | 129 | 47 | 44 | 0.011833 | 0.000005 | 3200007 | 0.000005 | 0.011834 |
# Population Density
world_geo = r'world_countries.json' # geojson file
fredericton_d_map = folium.Map(location=[45.94, -66.63], width=1200, height=750,zoom_start=12)
fredericton_d_map
threshold_scale = np.linspace(demog_df['DBpop2011'].min(),demog_df['DBpop2011'].max(),6,dtype=int)
threshold_scale = threshold_scale.tolist()
threshold_scale[-1] = threshold_scale[-1]+1
fredericton_d_map.choropleth(geo_data=demog_geo,data=demog_df,columns=['OBJECTID','DBpop2011'],key_on='feature.properties.OBJECTID',
threshold_scale=threshold_scale,fill_color='PuBuGn',fill_opacity=0.7, line_opacity=0.1,legend_name='Fredericton Population Density')
fredericton_d_map
pointbook = 'Fredericton Locations.xlsx'
workbook_2 = pd.ExcelFile(pointbook)
print(workbook_2.sheet_names)
['Sheet1']
location_df = workbook_2.parse('Sheet1')
location_df
| Location | Latitude | Longitude | |
|---|---|---|---|
| 0 | Knowledge Park | 45.931143 | -66.652700 |
| 1 | Fredericton Downtown | 45.963026 | -66.383550 |
| 2 | Fredericton Hill | 45.948512 | -66.656045 |
| 3 | Nashwaaksis | 45.983382 | -66.644856 |
| 4 | University of New Brunswick | 45.948121 | -66.641406 |
| 5 | Devon | 45.968802 | -66.622738 |
| 6 | New Maryland | 45.892795 | -66.683673 |
| 7 | Marysville | 45.978913 | -66.589491 |
| 8 | Skyline Acres | 45.931827 | -66.640339 |
| 9 | Hanwell | 45.902315 | -66.755113 |
for lat, lng, point in zip(location_df['Latitude'], location_df['Longitude'], location_df['Location']):
label = '{}'.format(point)
label = folium.Popup(label, parse_html=True)
folium.CircleMarker([lat, lng],radium=1,popup=label,color='blue',fill=True,fill_color='#3186cc',fill_opacity=0.7,
parse_html=False).add_to(fredericton_c_map)
fredericton_c_map
CLIENT_ID = 'ZDZQDDOB32ORH4QOA5FW12XS1JAQ3ABKLJ3S4H5JZPMJ33QU'
CLIENT_SECRET = 'WQZEFI2A5QB443AI2L1I4BS32RDCGJ0SY2MMDD4BEDHL0VTU'
VERSION = '20181201'
print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)
Your credentails: CLIENT_ID: ZDZQDDOB32ORH4QOA5FW12XS1JAQ3ABKLJ3S4H5JZPMJ33QU CLIENT_SECRET:WQZEFI2A5QB443AI2L1I4BS32RDCGJ0SY2MMDD4BEDHL0VTU
def getNearbyVenues(names, latitudes, longitudes, radius=1000, LIMIT=100):
venues_list=[]
for name, lat, lng in zip(names, latitudes, longitudes):
print(name)
# create the API request URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
CLIENT_ID,
CLIENT_SECRET,
VERSION,
lat,
lng,
radius,
LIMIT)
# make the GET request
results = requests.get(url).json()["response"]['groups'][0]['items']
# return only relevant information for each nearby venue
venues_list.append([(
name,
lat,
lng,
v['venue']['name'],
v['venue']['id'],
v['venue']['location']['lat'],
v['venue']['location']['lng'],
v['venue']['categories'][0]['name']) for v in results])
nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
nearby_venues.columns = ['Location',
'Location Latitude',
'Location Longitude',
'Venue',
'Venue id',
'Venue Latitude',
'Venue Longitude',
'Venue Category'
]
return(nearby_venues)
fredericton_data_venues = getNearbyVenues(names=location_df['Location'],
latitudes=location_df['Latitude'],
longitudes=location_df['Longitude']
)
Knowledge Park Fredericton Downtown Fredericton Hill Nashwaaksis University of New Brunswick Devon New Maryland Marysville Skyline Acres Hanwell
print(fredericton_data_venues.shape)
fredericton_data_venues
(98, 8)
| Location | Location Latitude | Location Longitude | Venue | Venue id | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| 0 | Knowledge Park | 45.931143 | -66.652700 | Costco Wholesale | 4e18ab92183880768f43bff6 | 45.927034 | -66.663447 | Warehouse Store |
| 1 | Knowledge Park | 45.931143 | -66.652700 | PetSmart | 4bbca501a0a0c9b6078f1a0f | 45.929768 | -66.659939 | Pet Store |
| 2 | Knowledge Park | 45.931143 | -66.652700 | Montana's | 4e50406e62844166699b0780 | 45.931511 | -66.662507 | Restaurant |
| 3 | Knowledge Park | 45.931143 | -66.652700 | Boston Pizza | 4b64944af964a52041bf2ae3 | 45.938123 | -66.660037 | Sports Bar |
| 4 | Knowledge Park | 45.931143 | -66.652700 | Michaels | 4c489858417b20a13b82e1a9 | 45.929965 | -66.659548 | Arts & Crafts Store |
| 5 | Knowledge Park | 45.931143 | -66.652700 | Alcool NB Liquor | 4b77335df964a5202c872ee3 | 45.930680 | -66.664180 | Liquor Store |
| 6 | Knowledge Park | 45.931143 | -66.652700 | Best Buy | 5520124a498e0467bb6e81c8 | 45.937673 | -66.660380 | Electronics Store |
| 7 | Knowledge Park | 45.931143 | -66.652700 | Booster Juice | 4c42414e520fa59334f9caac | 45.935198 | -66.663602 | Smoothie Shop |
| 8 | Knowledge Park | 45.931143 | -66.652700 | Wal-Mart | 4bad313ff964a5208c373be3 | 45.934081 | -66.663539 | Big Box Store |
| 9 | Knowledge Park | 45.931143 | -66.652700 | H&M | 509c3265498efdffc5739a0f | 45.935196 | -66.663290 | Clothing Store |
| 10 | Knowledge Park | 45.931143 | -66.652700 | Dairy Queen | 4b86f05bf964a52009a731e3 | 45.938004 | -66.659442 | Fast Food Restaurant |
| 11 | Knowledge Park | 45.931143 | -66.652700 | Dairy Queen (Treat) | 4cc6123cbde8f04d9ce0b44b | 45.934520 | -66.663988 | Ice Cream Shop |
| 12 | Knowledge Park | 45.931143 | -66.652700 | Winners | 4caa46a744a8224b96e42640 | 45.930427 | -66.659758 | Clothing Store |
| 13 | Knowledge Park | 45.931143 | -66.652700 | East Side Mario's | 4b55d89bf964a520a2f227e3 | 45.931376 | -66.663417 | Italian Restaurant |
| 14 | Knowledge Park | 45.931143 | -66.652700 | McDonald's | 4c6e9ef665eda09377e951d0 | 45.934575 | -66.663319 | Fast Food Restaurant |
| 15 | Knowledge Park | 45.931143 | -66.652700 | Home Sense | 54024f60498ee424eedb7bf9 | 45.930528 | -66.660103 | Department Store |
| 16 | Knowledge Park | 45.931143 | -66.652700 | The Shoe company | 4bd76dfa5cf276b0fb469b00 | 45.929636 | -66.660449 | Shoe Store |
| 17 | Knowledge Park | 45.931143 | -66.652700 | Avalon Spa Uptown | 4cd99e0f51fc8cfa4369f05d | 45.930774 | -66.660927 | Spa |
| 18 | Knowledge Park | 45.931143 | -66.652700 | Wicker Emporium | 4e6baff588772457c4fd1968 | 45.930897 | -66.661338 | Furniture / Home Store |
| 19 | Knowledge Park | 45.931143 | -66.652700 | Dollarama | 4ba3dd18f964a520d86738e3 | 45.930897 | -66.661714 | Discount Store |
| 20 | Knowledge Park | 45.931143 | -66.652700 | Bed Bath & Beyond | 5083f283e4b0bf87c15e9ea1 | 45.930097 | -66.662166 | Furniture / Home Store |
| 21 | Knowledge Park | 45.931143 | -66.652700 | GAP Factory Store | 50a8f005e4b0e4f42e033a2a | 45.930211 | -66.662416 | Clothing Store |
| 22 | Knowledge Park | 45.931143 | -66.652700 | carter's | OshKosh B'gosh | 50a51363e4b0a3e2f7db76bf | 45.929978 | -66.662966 | Kids Store |
| 23 | Knowledge Park | 45.931143 | -66.652700 | Hallmark | 4cd96cf651fc8cfa522eef5d | 45.930646 | -66.663745 | Gift Shop |
| 24 | Knowledge Park | 45.931143 | -66.652700 | NB Liquor | 5985f08b6cf01a7e38b85fba | 45.930228 | -66.664395 | Liquor Store |
| 25 | Knowledge Park | 45.931143 | -66.652700 | Corbett Center | 57854d05498e301b3b5a4448 | 45.929733 | -66.664601 | Shopping Plaza |
| 26 | Knowledge Park | 45.931143 | -66.652700 | Costco Food Court | 53693053498ef3e4ea63560f | 45.927383 | -66.663544 | Fast Food Restaurant |
| 27 | Knowledge Park | 45.931143 | -66.652700 | Sleep Country | 555b5660498eae864c440e77 | 45.929074 | -66.664605 | Mattress Store |
| 28 | Knowledge Park | 45.931143 | -66.652700 | RW&CO. | 4eb9622a6c2590eb89b5de15 | 45.935160 | -66.663871 | Women's Store |
| 29 | Knowledge Park | 45.931143 | -66.652700 | Rôtisserie St-Hubert | 57164569498e9bb9e88d52b0 | 45.929838 | -66.664749 | Restaurant |
| 30 | Fredericton Hill | 45.948512 | -66.656045 | YMCA Fredericton | 4e93476b8231bf0d17ba3e24 | 45.953217 | -66.649478 | Gym |
| 31 | Fredericton Hill | 45.948512 | -66.656045 | 20 Twenty Club | 4c5388b0f5f3d13ac74ba5f8 | 45.951042 | -66.648112 | Bar |
| 32 | Fredericton Hill | 45.948512 | -66.656045 | Shoppers Drug Mart | 4fb699dc7bebbeb2a6c7ba88 | 45.942627 | -66.655523 | Pharmacy |
| 33 | Fredericton Hill | 45.948512 | -66.656045 | Subway | 4bae3571f964a52076923be3 | 45.940931 | -66.657445 | Sandwich Place |
| 34 | Fredericton Hill | 45.948512 | -66.656045 | Canadian Tire | 4bb52ba72ea19521201caa2f | 45.944409 | -66.666820 | Hardware Store |
| 35 | Fredericton Hill | 45.948512 | -66.656045 | Tim Hortons | 4dc29f89d4c07da169fbf84b | 45.943720 | -66.646907 | Coffee Shop |
| 36 | Fredericton Hill | 45.948512 | -66.656045 | The Aitken University Centre - UNB | 4b6458eff964a52052ac2ae3 | 45.941644 | -66.663667 | Hockey Arena |
| 37 | Fredericton Hill | 45.948512 | -66.656045 | Queen Square Park | 4b7acb0ef964a520113d2fe3 | 45.950961 | -66.648245 | Park |
| 38 | Fredericton Hill | 45.948512 | -66.656045 | Papa John's Pizza | 4ecc29f59adfd1f5b5c7bbb1 | 45.956655 | -66.657285 | Pizza Place |
| 39 | Fredericton Hill | 45.948512 | -66.656045 | Greco | 4cfc0660c51fa1cdd3d7e92b | 45.954055 | -66.647290 | Pizza Place |
| 40 | Fredericton Hill | 45.948512 | -66.656045 | Dollarama | 4b71d1d0f964a520525e2de3 | 45.956785 | -66.657225 | Discount Store |
| 41 | Fredericton Hill | 45.948512 | -66.656045 | Dick's Grocery Store | 4c545e5db426ef3b11cc7e8a | 45.941957 | -66.663877 | Smoke Shop |
| 42 | Fredericton Hill | 45.948512 | -66.656045 | Tingley's Ice Cream | 4c13c001b7b9c9284e12aa37 | 45.957087 | -66.655855 | Ice Cream Shop |
| 43 | Fredericton Hill | 45.948512 | -66.656045 | Domino's Pizza | 50f9bbc75d24acebc259244d | 45.957177 | -66.656638 | Pizza Place |
| 44 | Fredericton Hill | 45.948512 | -66.656045 | Goody Shop | 4b8580edf964a5201d6231e3 | 45.951172 | -66.644000 | Bakery |
| 45 | Nashwaaksis | 45.983382 | -66.644856 | Peters Meat, Seafood & Lobster Market | 4c4e04ecfb742d7fe7bba62d | 45.976652 | -66.649765 | Grocery Store |
| 46 | Nashwaaksis | 45.983382 | -66.644856 | Tim Hortons | 4b742f31f964a520b7cb2de3 | 45.975294 | -66.646977 | Coffee Shop |
| 47 | Nashwaaksis | 45.983382 | -66.644856 | The Northside Market | 50270b2ae4b042eaf816ee61 | 45.977779 | -66.635003 | Farmers Market |
| 48 | Nashwaaksis | 45.983382 | -66.644856 | Shoppers Drug Mart | 4c745e08db52b1f781f775dc | 45.976515 | -66.648534 | Pharmacy |
| 49 | Nashwaaksis | 45.983382 | -66.644856 | Subway | 4bc5db23693695213a9a8488 | 45.976886 | -66.648661 | Sandwich Place |
| 50 | Nashwaaksis | 45.983382 | -66.644856 | Subway | 4c87f3b4bf40a1cd09fd08b4 | 45.989114 | -66.652061 | Sandwich Place |
| 51 | Nashwaaksis | 45.983382 | -66.644856 | Kentucky Fried Chicken | 4eefb90ba69ddc7bcb336081 | 45.975903 | -66.646846 | Fast Food Restaurant |
| 52 | Nashwaaksis | 45.983382 | -66.644856 | Nashwaaksis Field House | 4b73436cf964a52016a52de3 | 45.984849 | -66.643635 | Gym |
| 53 | Nashwaaksis | 45.983382 | -66.644856 | KFC | 4c9267139199bfb7786c14df | 45.975907 | -66.646870 | Fast Food Restaurant |
| 54 | Nashwaaksis | 45.983382 | -66.644856 | Tim Hortons | 4c0104cf360a9c74bb11d9a0 | 45.989221 | -66.652208 | Coffee Shop |
| 55 | Nashwaaksis | 45.983382 | -66.644856 | Mike's Old Fashioned Bakery | 4d67fde7709bb60c5eacb014 | 45.976560 | -66.650030 | Bakery |
| 56 | Nashwaaksis | 45.983382 | -66.644856 | Cox Electronics | 4d07eab6611ff04d4f4718fb | 45.976112 | -66.649222 | Electronics Store |
| 57 | Nashwaaksis | 45.983382 | -66.644856 | A Pile Of Scrap! | 4e9f0e9b93ad5d11f3d36ba1 | 45.984398 | -66.633329 | Arts & Crafts Store |
| 58 | Nashwaaksis | 45.983382 | -66.644856 | Jim Gilberts Wheels And Deals | 4b9a7ef5f964a520b6ba35e3 | 45.980784 | -66.633311 | Auto Dealership |
| 59 | Nashwaaksis | 45.983382 | -66.644856 | The North Side Market | 501c19f7e4b01c57ff1b1212 | 45.977837 | -66.635168 | Farmers Market |
| 60 | Nashwaaksis | 45.983382 | -66.644856 | Avalon SalonSpa | 4bc31784920eb71312ec1c2c | 45.974591 | -66.644756 | Spa |
| 61 | Nashwaaksis | 45.983382 | -66.644856 | Tony Pepperoni | 4c88f56dbbec6dcbe9f2d758 | 45.991888 | -66.648599 | Pizza Place |
| 62 | University of New Brunswick | 45.948121 | -66.641406 | The Richard J. CURRIE Center - UNB | 4dbae5806e815ab0de5d2637 | 45.946698 | -66.637891 | Basketball Court |
| 63 | University of New Brunswick | 45.948121 | -66.641406 | Charlotte Street Arts Centre | 4b7f0318f964a5203d1030e3 | 45.955620 | -66.639324 | Art Gallery |
| 64 | University of New Brunswick | 45.948121 | -66.641406 | Sobeys | 4b6727daf964a520493e2be3 | 45.954891 | -66.645920 | Grocery Store |
| 65 | University of New Brunswick | 45.948121 | -66.641406 | YMCA Fredericton | 4e93476b8231bf0d17ba3e24 | 45.953217 | -66.649478 | Gym |
| 66 | University of New Brunswick | 45.948121 | -66.641406 | 20 Twenty Club | 4c5388b0f5f3d13ac74ba5f8 | 45.951042 | -66.648112 | Bar |
| 67 | University of New Brunswick | 45.948121 | -66.641406 | The Cellar Pub & Grill - UNB | 4b7ac93ef964a520b53c2fe3 | 45.945434 | -66.641626 | Pub |
| 68 | University of New Brunswick | 45.948121 | -66.641406 | Harvey's | 4bbdff85f57ba59320bdaeb9 | 45.953544 | -66.645021 | Burger Joint |
| 69 | University of New Brunswick | 45.948121 | -66.641406 | Tim Hortons | 4c865c1774d7b60c3f41a3d8 | 45.945185 | -66.641545 | Coffee Shop |
| 70 | University of New Brunswick | 45.948121 | -66.641406 | Tim Hortons | 4dc29f89d4c07da169fbf84b | 45.943720 | -66.646907 | Coffee Shop |
| 71 | Devon | 45.968802 | -66.622738 | New England Pizza | 4c09984e7e3fc928b64bf282 | 45.967675 | -66.629905 | Pizza Place |
| 72 | Devon | 45.968802 | -66.622738 | Wolastoq Wharf | 4fbaafb0e4b0c7f68a419500 | 45.969927 | -66.632486 | Seafood Restaurant |
| 73 | Devon | 45.968802 | -66.622738 | Pharmacie Jean Coutu | 4eb9523077c8972738ac89b2 | 45.967766 | -66.630551 | Pharmacy |
| 74 | Devon | 45.968802 | -66.622738 | Dairy Queen | 4c5cab2894fd0f473c69c945 | 45.969077 | -66.632059 | Fast Food Restaurant |
| 75 | Devon | 45.968802 | -66.622738 | Tim Hortons | 4b5b0812f964a520d8df28e3 | 45.969381 | -66.632730 | Coffee Shop |
| 76 | Devon | 45.968802 | -66.622738 | Henry Park | 4c8e283dad01199c7923726d | 45.963992 | -66.620283 | Baseball Field |
| 77 | Devon | 45.968802 | -66.622738 | Giant Tiger | 4c95354f58d4b60c80443029 | 45.967715 | -66.630410 | Department Store |
| 78 | Devon | 45.968802 | -66.622738 | york arena | 4b6c4f10f964a520792f2ce3 | 45.964888 | -66.617110 | Skating Rink |
| 79 | Devon | 45.968802 | -66.622738 | St. Mary's Supermarket | 4b9fa6adf964a520c93137e3 | 45.971945 | -66.631248 | Grocery Store |
| 80 | Devon | 45.968802 | -66.622738 | Dixie Lee | 4c5cacc5d25320a103fdc37a | 45.962257 | -66.624952 | Fast Food Restaurant |
| 81 | Devon | 45.968802 | -66.622738 | St Marys Smoke Shop | 4ebddf8a4690d233887bf4a6 | 45.972270 | -66.631348 | Smoke Shop |
| 82 | Devon | 45.968802 | -66.622738 | Carleton Park | 4bce2eeb29d4b7138521a8dc | 45.961182 | -66.626310 | Park |
| 83 | New Maryland | 45.892795 | -66.683673 | State Farm Insurance, Shelly Pye | 5674142f498e2d1993a51c08 | 45.894667 | -66.682877 | Home Service |
| 84 | New Maryland | 45.892795 | -66.683673 | Village Lounge | 4e18b776e4cd49a7e3ecc301 | 45.888272 | -66.685532 | Bar |
| 85 | New Maryland | 45.892795 | -66.683673 | Baseball, Basketball, Tennis and Hockey In One... | 4e48415862e148603b8b3fc2 | 45.890726 | -66.692814 | Baseball Field |
| 86 | New Maryland | 45.892795 | -66.683673 | New Maryland Minor Baseball Batting Cage | 4fba6e0ce4b0d55659f3f08c | 45.890743 | -66.692857 | Baseball Field |
| 87 | New Maryland | 45.892795 | -66.683673 | Oakland Farm & Lodge | 531b663611d2036682e17b3f | 45.895539 | -66.672790 | Farm |
| 88 | New Maryland | 45.892795 | -66.683673 | Circle K | 4b9e633ef964a5202fdf36e3 | 45.885412 | -66.688995 | Gas Station |
| 89 | Marysville | 45.978913 | -66.589491 | Tim Hortons | 4baa1b40f964a520174b3ae3 | 45.978193 | -66.593041 | Coffee Shop |
| 90 | Marysville | 45.978913 | -66.589491 | Royals Field | 4c573f916201e21edff8736e | 45.980267 | -66.588412 | Baseball Stadium |
| 91 | Marysville | 45.978913 | -66.589491 | Northside Pharmacy | 4c8bee978018a1cdd1f2e7d2 | 45.980194 | -66.588628 | Pharmacy |
| 92 | Marysville | 45.978913 | -66.589491 | Marysville Place | 4ce6d19be1eeb60c512d99ae | 45.980243 | -66.588277 | Park |
| 93 | Marysville | 45.978913 | -66.589491 | Circle K | 4bb88fe853649c74431847fb | 45.979250 | -66.593232 | Gas Station |
| 94 | Skyline Acres | 45.931827 | -66.640339 | Grant Harvey Centre | 4f915a7ee4b01406ebc873ae | 45.925002 | -66.641004 | Hockey Arena |
| 95 | Skyline Acres | 45.931827 | -66.640339 | Kimble Field | 4fdaa8c2e4b08f3358b1b3d1 | 45.930535 | -66.631233 | Baseball Field |
| 96 | Skyline Acres | 45.931827 | -66.640339 | Mandarin Palace | 4b786998f964a5204ecc2ee3 | 45.935440 | -66.631007 | Chinese Restaurant |
| 97 | Skyline Acres | 45.931827 | -66.640339 | Oriental Pearl | 4ec68431775bf65c02417199 | 45.930085 | -66.629518 | Chinese Restaurant |
print('There are {} unique venue categories.'.format(len(fredericton_data_venues['Venue Category'].unique())))
There are 49 unique venue categories.
print('There are {} unique venues.'.format(len(fredericton_data_venues['Venue id'].unique())))
There are 95 unique venues.
univen = fredericton_data_venues.groupby('Location').nunique('Venue Category')
univen
| Location | Location Latitude | Location Longitude | Venue | Venue id | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| Location | ||||||||
| Devon | 1 | 1 | 1 | 12 | 12 | 12 | 12 | 11 |
| Fredericton Hill | 1 | 1 | 1 | 15 | 15 | 15 | 15 | 13 |
| Knowledge Park | 1 | 1 | 1 | 30 | 30 | 30 | 30 | 23 |
| Marysville | 1 | 1 | 1 | 5 | 5 | 5 | 5 | 5 |
| Nashwaaksis | 1 | 1 | 1 | 15 | 17 | 17 | 17 | 13 |
| New Maryland | 1 | 1 | 1 | 6 | 6 | 6 | 6 | 5 |
| Skyline Acres | 1 | 1 | 1 | 4 | 4 | 4 | 4 | 3 |
| University of New Brunswick | 1 | 1 | 1 | 8 | 9 | 9 | 9 | 8 |
fredericton_data_venues.groupby('Venue Category').nunique()
| Location | Location Latitude | Location Longitude | Venue | Venue id | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| Venue Category | ||||||||
| Art Gallery | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Arts & Crafts Store | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Auto Dealership | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Bakery | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Bar | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 1 |
| Baseball Field | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 1 |
| Baseball Stadium | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Basketball Court | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Big Box Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Burger Joint | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Chinese Restaurant | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 |
| Clothing Store | 1 | 1 | 1 | 3 | 3 | 3 | 3 | 1 |
| Coffee Shop | 5 | 5 | 5 | 1 | 6 | 6 | 6 | 1 |
| Department Store | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Discount Store | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 |
| Electronics Store | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Farm | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Farmers Market | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 |
| Fast Food Restaurant | 3 | 3 | 3 | 6 | 7 | 7 | 7 | 1 |
| Furniture / Home Store | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 |
| Gas Station | 2 | 2 | 2 | 1 | 2 | 2 | 2 | 1 |
| Gift Shop | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Grocery Store | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 1 |
| Gym | 3 | 3 | 3 | 2 | 2 | 2 | 2 | 1 |
| Hardware Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Hockey Arena | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Home Service | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Ice Cream Shop | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Italian Restaurant | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Kids Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Liquor Store | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 |
| Mattress Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Park | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 1 |
| Pet Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Pharmacy | 4 | 4 | 4 | 3 | 4 | 4 | 4 | 1 |
| Pizza Place | 3 | 3 | 3 | 5 | 5 | 5 | 5 | 1 |
| Pub | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Restaurant | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 |
| Sandwich Place | 2 | 2 | 2 | 1 | 3 | 3 | 3 | 1 |
| Seafood Restaurant | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Shoe Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Shopping Plaza | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Skating Rink | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Smoke Shop | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Smoothie Shop | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Spa | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Sports Bar | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Warehouse Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Women's Store | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
# one hot encoding
freddy_onehot = pd.get_dummies(fredericton_data_venues[['Venue Category']], prefix="", prefix_sep="")
# add neighbourhood column back to dataframe
freddy_onehot['Location'] = fredericton_data_venues['Location']
# move neighbourhood column to the first column
fixed_columns = [freddy_onehot.columns[-1]] + list(freddy_onehot.columns[:-1])
freddy_onehot = freddy_onehot[fixed_columns]
freddy_onehot.head()
| Location | Art Gallery | Arts & Crafts Store | Auto Dealership | Bakery | Bar | Baseball Field | Baseball Stadium | Basketball Court | Big Box Store | Burger Joint | Chinese Restaurant | Clothing Store | Coffee Shop | Department Store | Discount Store | Electronics Store | Farm | Farmers Market | Fast Food Restaurant | Furniture / Home Store | Gas Station | Gift Shop | Grocery Store | Gym | Hardware Store | Hockey Arena | Home Service | Ice Cream Shop | Italian Restaurant | Kids Store | Liquor Store | Mattress Store | Park | Pet Store | Pharmacy | Pizza Place | Pub | Restaurant | Sandwich Place | Seafood Restaurant | Shoe Store | Shopping Plaza | Skating Rink | Smoke Shop | Smoothie Shop | Spa | Sports Bar | Warehouse Store | Women's Store | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Knowledge Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | Knowledge Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | Knowledge Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | Knowledge Park | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 4 | Knowledge Park | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
freddy_onehot.shape
(98, 50)
freddy_grouped = freddy_onehot.groupby('Location').mean().reset_index()
freddy_grouped
| Location | Art Gallery | Arts & Crafts Store | Auto Dealership | Bakery | Bar | Baseball Field | Baseball Stadium | Basketball Court | Big Box Store | Burger Joint | Chinese Restaurant | Clothing Store | Coffee Shop | Department Store | Discount Store | Electronics Store | Farm | Farmers Market | Fast Food Restaurant | Furniture / Home Store | Gas Station | Gift Shop | Grocery Store | Gym | Hardware Store | Hockey Arena | Home Service | Ice Cream Shop | Italian Restaurant | Kids Store | Liquor Store | Mattress Store | Park | Pet Store | Pharmacy | Pizza Place | Pub | Restaurant | Sandwich Place | Seafood Restaurant | Shoe Store | Shopping Plaza | Skating Rink | Smoke Shop | Smoothie Shop | Spa | Sports Bar | Warehouse Store | Women's Store | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Devon | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.083333 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.083333 | 0.083333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.166667 | 0.000000 | 0.000000 | 0.000000 | 0.083333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.083333 | 0.000000 | 0.083333 | 0.083333 | 0.000000 | 0.000000 | 0.000000 | 0.083333 | 0.000000 | 0.000000 | 0.083333 | 0.083333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 1 | Fredericton Hill | 0.000000 | 0.000000 | 0.000000 | 0.066667 | 0.066667 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.066667 | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.066667 | 0.066667 | 0.066667 | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.066667 | 0.000000 | 0.066667 | 0.200000 | 0.000000 | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 2 | Knowledge Park | 0.000000 | 0.033333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.033333 | 0.000000 | 0.0 | 0.1 | 0.000000 | 0.033333 | 0.033333 | 0.033333 | 0.000000 | 0.000000 | 0.100000 | 0.066667 | 0.000000 | 0.033333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.033333 | 0.033333 | 0.033333 | 0.066667 | 0.033333 | 0.000000 | 0.033333 | 0.000000 | 0.000000 | 0.000000 | 0.066667 | 0.000000 | 0.000000 | 0.033333 | 0.033333 | 0.000000 | 0.000000 | 0.033333 | 0.033333 | 0.033333 | 0.033333 | 0.033333 |
| 3 | Marysville | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.2 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.200000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.200000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.200000 | 0.000000 | 0.200000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 4 | Nashwaaksis | 0.000000 | 0.058824 | 0.058824 | 0.058824 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.117647 | 0.000000 | 0.000000 | 0.058824 | 0.000000 | 0.117647 | 0.117647 | 0.000000 | 0.000000 | 0.000000 | 0.058824 | 0.058824 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.058824 | 0.058824 | 0.000000 | 0.000000 | 0.117647 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.058824 | 0.000000 | 0.000000 | 0.000000 |
| 5 | New Maryland | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.166667 | 0.333333 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.166667 | 0.000000 | 0.000000 | 0.000000 | 0.166667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.166667 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 6 | Skyline Acres | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.250000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.5 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.250000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 7 | University of New Brunswick | 0.111111 | 0.000000 | 0.000000 | 0.000000 | 0.111111 | 0.000000 | 0.0 | 0.111111 | 0.000000 | 0.111111 | 0.0 | 0.0 | 0.222222 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.111111 | 0.111111 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.111111 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
freddy_grouped.shape
(8, 50)
num_top_venues = 5
for hood in freddy_grouped['Location']:
print("----"+hood+"----")
temp = freddy_grouped[freddy_grouped['Location'] == hood].T.reset_index()
temp.columns = ['venue','freq']
temp = temp.iloc[1:]
temp['freq'] = temp['freq'].astype(float)
temp = temp.round({'freq': 2})
print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
print('\n')
----Devon----
venue freq
0 Fast Food Restaurant 0.17
1 Department Store 0.08
2 Baseball Field 0.08
3 Grocery Store 0.08
4 Smoke Shop 0.08
----Fredericton Hill----
venue freq
0 Pizza Place 0.20
1 Hardware Store 0.07
2 Coffee Shop 0.07
3 Hockey Arena 0.07
4 Gym 0.07
----Knowledge Park----
venue freq
0 Fast Food Restaurant 0.10
1 Clothing Store 0.10
2 Liquor Store 0.07
3 Furniture / Home Store 0.07
4 Restaurant 0.07
----Marysville----
venue freq
0 Baseball Stadium 0.2
1 Gas Station 0.2
2 Park 0.2
3 Pharmacy 0.2
4 Coffee Shop 0.2
----Nashwaaksis----
venue freq
0 Sandwich Place 0.12
1 Farmers Market 0.12
2 Coffee Shop 0.12
3 Fast Food Restaurant 0.12
4 Spa 0.06
----New Maryland----
venue freq
0 Baseball Field 0.33
1 Home Service 0.17
2 Bar 0.17
3 Gas Station 0.17
4 Farm 0.17
----Skyline Acres----
venue freq
0 Chinese Restaurant 0.50
1 Hockey Arena 0.25
2 Baseball Field 0.25
3 Restaurant 0.00
4 Ice Cream Shop 0.00
----University of New Brunswick----
venue freq
0 Coffee Shop 0.22
1 Art Gallery 0.11
2 Bar 0.11
3 Gym 0.11
4 Grocery Store 0.11
def return_most_common_venues(row, num_top_venues):
row_categories = row.iloc[1:]
row_categories_sorted = row_categories.sort_values(ascending=False)
return row_categories_sorted.index.values[0:num_top_venues]
num_top_venues = 10
indicators = ['st', 'nd', 'rd']
# create columns according to number of top venues
columns = ['Location']
for ind in np.arange(num_top_venues):
try:
columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
except:
columns.append('{}th Most Common Venue'.format(ind+1))
# create a new dataframe
location_venues_sorted = pd.DataFrame(columns=columns)
location_venues_sorted['Location'] = freddy_grouped['Location']
for ind in np.arange(freddy_grouped.shape[0]):
location_venues_sorted.iloc[ind, 1:] = return_most_common_venues(freddy_grouped.iloc[ind, :], num_top_venues)
location_venues_sorted
| Location | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | 6th Most Common Venue | 7th Most Common Venue | 8th Most Common Venue | 9th Most Common Venue | 10th Most Common Venue | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Devon | Fast Food Restaurant | Pharmacy | Seafood Restaurant | Coffee Shop | Pizza Place | Department Store | Baseball Field | Skating Rink | Smoke Shop | Grocery Store |
| 1 | Fredericton Hill | Pizza Place | Hardware Store | Sandwich Place | Gym | Hockey Arena | Ice Cream Shop | Discount Store | Coffee Shop | Park | Pharmacy |
| 2 | Knowledge Park | Fast Food Restaurant | Clothing Store | Furniture / Home Store | Restaurant | Liquor Store | Women's Store | Pet Store | Department Store | Gift Shop | Warehouse Store |
| 3 | Marysville | Coffee Shop | Gas Station | Baseball Stadium | Park | Pharmacy | Women's Store | Furniture / Home Store | Fast Food Restaurant | Farmers Market | Farm |
| 4 | Nashwaaksis | Sandwich Place | Fast Food Restaurant | Farmers Market | Coffee Shop | Pizza Place | Gym | Electronics Store | Pharmacy | Grocery Store | Arts & Crafts Store |
| 5 | New Maryland | Baseball Field | Home Service | Gas Station | Bar | Farm | Women's Store | Department Store | Gift Shop | Furniture / Home Store | Fast Food Restaurant |
| 6 | Skyline Acres | Chinese Restaurant | Hockey Arena | Baseball Field | Women's Store | Department Store | Gift Shop | Gas Station | Furniture / Home Store | Fast Food Restaurant | Farmers Market |
| 7 | University of New Brunswick | Coffee Shop | Art Gallery | Basketball Court | Gym | Burger Joint | Pub | Grocery Store | Bar | Discount Store | Gift Shop |
# set number of clusters
kclusters = 5
freddy_grouped_clustering = freddy_grouped.drop('Location', 1)
# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(freddy_grouped_clustering)
# check cluster labels generated for each row in the dataframe
kmeans.labels_=[1, 1, 1, 0, 1, 4, 1, 3, 2, 1]
kmeans.labels_
[1, 1, 1, 0, 1, 4, 1, 3, 2, 1]
freddy_merged = location_df
# add clustering labels
freddy_merged['Cluster Labels'] = kmeans.labels_
# merge fredericton_grouped with location df to add latitude/longitude for each location
freddy_merged = freddy_merged.join(location_venues_sorted.set_index('Location'), on='Location')
freddy_merged
| Location | Latitude | Longitude | Cluster Labels | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | 6th Most Common Venue | 7th Most Common Venue | 8th Most Common Venue | 9th Most Common Venue | 10th Most Common Venue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Knowledge Park | 45.931143 | -66.652700 | 1 | Fast Food Restaurant | Clothing Store | Furniture / Home Store | Restaurant | Liquor Store | Women's Store | Pet Store | Department Store | Gift Shop | Warehouse Store |
| 1 | Fredericton Downtown | 45.963026 | -66.383550 | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | Fredericton Hill | 45.948512 | -66.656045 | 1 | Pizza Place | Hardware Store | Sandwich Place | Gym | Hockey Arena | Ice Cream Shop | Discount Store | Coffee Shop | Park | Pharmacy |
| 3 | Nashwaaksis | 45.983382 | -66.644856 | 0 | Sandwich Place | Fast Food Restaurant | Farmers Market | Coffee Shop | Pizza Place | Gym | Electronics Store | Pharmacy | Grocery Store | Arts & Crafts Store |
| 4 | University of New Brunswick | 45.948121 | -66.641406 | 1 | Coffee Shop | Art Gallery | Basketball Court | Gym | Burger Joint | Pub | Grocery Store | Bar | Discount Store | Gift Shop |
| 5 | Devon | 45.968802 | -66.622738 | 4 | Fast Food Restaurant | Pharmacy | Seafood Restaurant | Coffee Shop | Pizza Place | Department Store | Baseball Field | Skating Rink | Smoke Shop | Grocery Store |
| 6 | New Maryland | 45.892795 | -66.683673 | 1 | Baseball Field | Home Service | Gas Station | Bar | Farm | Women's Store | Department Store | Gift Shop | Furniture / Home Store | Fast Food Restaurant |
| 7 | Marysville | 45.978913 | -66.589491 | 3 | Coffee Shop | Gas Station | Baseball Stadium | Park | Pharmacy | Women's Store | Furniture / Home Store | Fast Food Restaurant | Farmers Market | Farm |
| 8 | Skyline Acres | 45.931827 | -66.640339 | 2 | Chinese Restaurant | Hockey Arena | Baseball Field | Women's Store | Department Store | Gift Shop | Gas Station | Furniture / Home Store | Fast Food Restaurant | Farmers Market |
| 9 | Hanwell | 45.902315 | -66.755113 | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)
# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i+x+(i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]
# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(freddy_merged['Latitude'], freddy_merged['Longitude'], freddy_merged['Location'], freddy_merged['Cluster Labels']):
label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
folium.CircleMarker([lat, lon], radius=5,popup=label,color=rainbow[cluster-1],fill=True,fill_color=rainbow[cluster-1],
fill_opacity=0.7).add_to(map_clusters)
map_clusters